Introduction to Bayesian variable selection methods in high-dimensional omics data analysis
10.3760/cma.j.issn.0254-6450.2017.05.025
- VernacularTitle:高维组学数据分析中的贝叶斯变量选择方法
- Author:
Xiaoqiang DONG
1
;
Shuhong XU
;
Ran TAO
;
Tong WANG
Author Information
1. 山西医科大学公共卫生学院卫生统计教研室
- Keywords:
High-dimensional data;
Bayesian variable selection;
g-prior;
Non-local prior
- From:
Chinese Journal of Epidemiology
2017;38(5):679-683
- CountryChina
- Language:Chinese
-
Abstract:
With the rapid development of genome sequencing technology and bioinformatics in recent years,it has become possible to measure thousands of omics data which might be associated with the progress of diseases,i.e."high-dimensional data".This type of omics data have a common feature that the number of variable p is usually greater than the observation cases n,and often has high correlation between independent variables.Therefore,it is a great statistical challenge to identify really meaningful variables from omics data.This paper summarizes the methods of Bayesian variable selection in the analysis of high-dimensional data.
